The present invention relates generally to remote sensing and more particularly to methods for detecting objects of approximately known size in conditions of low signal-to-noise ratio.
Detection and tracking of a moving object is a problem of interest in surveillance systems. The major complications arise when signal-to-noise ratio is extremely low and the time for making a decision is limited.
The problem of surveillance is formulated as follows. At any moment, a field of view is presented as a frame of data points. An object in the field of view corresponds to a set of adjacent data points of the frame. The frame can be displayed on a screen as an image: one data point of the frame corresponds to one pixel of the image. Every T milliseconds, a detection system receives a new frame of the same field of view. The goal of detection is to identify if the object is in the field of view and to estimate the position and velocity of the object at the moment. The goal of tracking is to estimate the position and velocity of the object at each moment after the object has been detected.
Methods for solving problems of this type are described in, for example, Fatih M. Porikli et al., U.S. Patent Application 20100246997 or Matthew Orton and William Fitzgerald, IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 50, NO. 2, FEBRUARY 2002. Methods of using a-priori information for detection and tracking of moving objects are described, for example, by Franz Meyer et al. in CMRT05. IAPRS, Vol. XXXVI, Part 3/W24, 2005.
One of the most powerful approaches is Bayesian particle filtering. Each particle forms an independent hypothesis of the state of the object (its azimuth, speed, x-coordinate, and y-coordinate) at a given time. This method estimates a sequence of actual states of the object based on a sequence of observed states.
The programming implementation of the Bayesian method is capable of dealing with 10,000 particles in 55 frames per second. Computations are parallelized in Compute Unified Device Architecture (CUDA); see Matthew A. Goodrum et al., 3rd Workshop on EAMA in conjunction with ISCA 2010.
From an implementation point of view, this method has a bottleneck: about 90% of the time the program spends on update of particles' weights. Particles weighs are being updated because the number of specified hypotheses is very big (it is equal to the product of four numbers: the number of all possible azimuths, speeds, x-coordinates, and y-coordinates of the object), so it is computationally infeasible to follow all of them.
From a conceptual point of view, this method as well as other known methods have a room for improvement because they do not use the fact that the object is a compact set of adjacent data points of the frame.